Systems and Methods for Identifying Morphological Patterns in Tissue Samples

2. The method of claim 1, wherein the one or more two-dimensional images comprises a plurality of two-dimensional images and each two-dimensional image of the plurality of two-dimensional images is displayed, co-aligned with (i) the first indicia for each probe spot in the plurality of probe spots that have been assigned to the first cluster and (ii) the second indicia for each probe spot in the plurality of probe spots that have been assigned to the second cluster.

3. The method of claim 2, the method further comprising, responsive to receiving user display instructions, displaying or removing from display one or more two-dimensional images in the plurality of two-dimensional images.

4. The method of claim 1, wherein the one or more two-dimensional images comprises a plurality of two-dimensional images and each respective two-dimensional image in the plurality of two-dimensional images is acquired from the first tissue section using a different wavelength or different wavelength band.

10. The method of claim 1, wherein the obtaining B) comprises clustering all or a subset of the probe spots in the plurality of probe spots using the discrete attribute values assigned to each respective probe spot as a multi-dimensional vector, wherein the clustering is configured to load less than the entirety of the discrete attribute value dataset into a non-persistent memory during the clustering thereby allowing the clustering of the discrete attribute value dataset having a size that exceeds storage space in a non-persistent memory allocated to the discrete attribute value dataset.

11. The method of claim 1, wherein each respective cluster in the plurality of clusters consists of a unique subset of the plurality of probe spots.

13. The method of claim 12, wherein the discrete attribute value dataset represents a whole transcriptome sequencing experiment that quantifies gene expression in counts of transcript reads mapped to the plurality of genes or the discrete attribute value dataset represents a targeted transcriptome sequencing experiment that quantifies gene expression in UMI counts mapped to probes in the plurality of probes.

16. The method of claim 1, wherein each respective probe spot in the plurality of probe spots has a center to center distance to a neighboring probe spot in the plurality of probe spots of 100 μM or less.

17. The method of claim 1, the method further comprising, for each respective probe spot overlayed on the first two-dimensional image, displaying a corresponding discrete attribute value, associated with the respective probe spot, for a first loci in the plurality of loci.

18. The method of claim 17, wherein the corresponding discrete attribute value is a unique molecular identifier count indicating a number of copies of a product of the first loci that were detected in the respective probe spot.

19. The method of claim 17, wherein the corresponding discrete attribute value is displayed in color coded log-space in accordance with a log-space heat map scale.